Download Pollution and Climate Change

Pollution and Climate
Change
ALLISON LARR, COLUMBIA
MAT THEW NEIDELL, COLUMBIA AND NBER
Climate change  air quality  child
well-being
Climate change and ozone
◦ Ozone = f(VOC, NOx, temperature, sunlight)
◦ If unabated: higher temperatures  higher ozone
Climate change mitigation and pollution (co-benefits)
◦ Fossil fuels
◦ CO2 emissions
◦ Criteria pollutants (PM2.5, SO2, NOx)
◦ If abated: less fossil fuels  less PM2.5 and ozone
◦ E.g. Clean power plan: reduce SO2 and NOx
Air pollution and well being
◦ Early years: birth weight, gestational length, infant mortality
◦ Later years
◦ Respiratory diseases. E.g., asthma
◦ Performance in school, labor market
Outline
1. Review studies on climate and air quality
◦ Overview of AQ projections
◦ Studies that project both:
◦ PM2.5 and ozone
◦ BAU and mitigation
2. Review QE studies on PM2.5/ozone and well-being
◦ Pollution not randomly assigned. E.g., sorting
◦ QE studies limit OVB
3. Combine 1 & 2 above to project future changes in well-being
◦ Fraught with heroic assumptions
◦ Need evidence in light of uncertainty
4. Comment on other (developing) nations
AQ modeling: 4 steps
1. Carbon emissions: IPCC scenarios (A1, A2, B1, B2)
◦ Fossil fuel reliance
◦ Economic and population growth
◦ Technological progress
2. Climate change projections
◦ General circulation model (GCM) of earth’s climate: temperature, precipitation, etc.
◦ Results on coarse spatial resolution, ignore local conditions. E.g., 100 km2, topography
3. Downscaling
◦ Modify spatial resolution: country, state, city, etc.
◦ Provide local weather
4. AQ models: Community Multi-scale Air Quality (CMAQ) model
◦ Simulates chemical and physical processes involved in atmospheric chemical transport
◦ Combine emissions & weather  local pollution
Table 1. Ozone and PM2.5 projection
model scenarios
Projection
Emissions Scenario(s)
Climate Models and Downscaling Air Quality Models
Period
Chen et al. 2004
IPCC A2
PCM / MM5
CMAQ / MOZART-2
2045-2054
Hogrefe et al. 2004
IPCC A2
GISS GCM / SMOKE / MM5
CMAQ
2053-2057
Avise et al. 2009
IPCC A2
PCM/MM5
CMAQ
2045-2054
Tao et al. 2007
IPCC A1Fi and B1
PCM / MM5
SAQM
2050
Nolte et al. 2008
IPCC A1B and current emissions
GISS GCM / MM5
CMAQ
2045-2055
Tagaris et al. 2007
IPCC A1B and current emissions
GISS GCM / MM5
CMAQ
2049-2051
Trail et al. 2014
RCS 4.5
GISS GCM / WRF
CMAQ
2048-2052
Penrod et al. 2014
IPCC A1B
WRF
CMAQ
2030
Abbreviations: PCM - Parallel Climate Model; MM5 - Fifth-Generation Penn State/NCAR Mesoscale Model; GISS GCM - Goddard Institute
of Space Studies General Circulation Model; SMOKE - Sparse Matrix Operator Kernel Emissions Model; CMAQ - Community Multi-Scale Air
Quality Model; MOZART-2 - Model for OZone and Related chemical Tracers; SAQM - SJVAQS-AUSPEX RegionalModeling Adaptation
Project Air Quality Model
How does AQ affect children?
Health effects
◦ Ozone: lung irritant  shortness of breath, lung inflammation, asthma exacerbation
◦ PM2.5: travels though lungs into bloodstream
◦ Respiratory effects
◦ Cardiovascular effects (heart attacks, blood pressure, etc.)
Human capital effects
◦ Indirectly via health effects
◦ Direct effects: PM2.5 travels up olfactory nerves to brain
◦ Latent effects via epigenetic changes (FOH)
Quasi-experimental approach
Major concern: sorting
◦ AQ higher in more productive areas (e.g., cities)  wealthier families live in dirtier areas
◦ AQ capitalized into housing prices  wealthier families live in cleaner areas
Two approaches
◦ Policy event
◦ 1980-82 recession
◦ CAAA as an instrument
◦ Military relocation
◦ Area fixed effects
◦ People sort on average pollution
◦ SR variation within area exogenous
Focus on QE ozone and PM (TSP, PM10, PM2.5) studies
Author
Outcome
Pollutant
Contemporaneous exposure & infant health
Chay and Greenstone (2003a) infant mortality TSP
Chay and Greenstone (2003b) infant mortality TSP
Sanders & Stoecker (2011)
sex ratio
TSP
Janke et al. (2009)
<15 mortality PM10
Knittel et al. (2011)
infant mortality PM10
Arceo-Gomez et al. (2012)
infant mortality PM10
Contemporaneous exposure & child health
Lleras-Muney (2010)
hospitalizations ozone
Neidell (2009)
hospitalizations ozone
Beatty and Shimshack (2014) hospitalizations ozone
Contemporaneous exposure & human capital
Zweig et al. (2009)
test scores
PM2.5
Lavy et al. (2012)
test scores
PM2.5
Early exposure & later human capital, productivity
Sanders (2012)
test scores
TSP
Isen et al. (2013)
earnings
TSP
Design
Magnitude
recession
CAAA
CAAA
fixed effects
fixed effects
fixed effects
1 unit TSPs --> 4-7 more infant deaths per 100,000 births
1 unit TSPs --> 5-8 more infant deaths per 100,000 births
35 unit TSPs --> male births increase by 3.1 percentage points
10 unit PM10 --> 4 fewer deaths per 100,000 children
1 unit PM10 --> 18 fewer infant deaths per 100,000 live births
1 unit PM10 --> 0.24 more infant deaths per 100,000 births
relocation
.008 ppm ozone --> respiratory hospitalization decrease by 8-23 percent
fixed effects 0.01 ppm ozone --> hospital admissions increase by 1.09%, 2.88% with alerts
fixed effects .0024 ppm ozone --> respiratory treatment increase by 2.5–3.3 percent
fixed effects 10% PM2.5 --> increased math by 0.34% and reading by 0.21%
fixed effects 10 unit PM2.5 --> reduces test scores by .46 points (1.9% of SD)
recession
CAAA
1 SD TSPs --> high school test scores decrease by 6% of SD
10 unit TSPs --> reduce earnings by 1%
Projections
Focus on Tagaris et al. (2007)
◦
◦
◦
◦
Baseline (2001), BAU (2050), and mitigation (2050)
Same model for PM2.5 and ozone
Regional projections
Smaller unabated ozone projections than others: 1 vs. 5 ppb
Projections for:
◦ PM2.5 and infant mortality
◦ PM2.5 and adult earnings
◦ Ozone and hospitalizations
Table 2. Ozone and PM2.5 Projections by
Region
year
2001
2050
2050 BAU
West
M8hO3
(ppb)
49.75
46.25
49.75
PM2.5
(μg/m3)
4.05
3.65
4.15
Plains
M8hO3
(ppb)
48.25
44.25
49
PM2.5
(μg/m3)
6.925
5.425
6.875
Midwest
M8hO3
(ppb)
45.25
40.5
45.25
PM2.5
(μg/m3)
11.725
9.025
12.2
Northeast
Southeast
US
M8hO3
PM2.5
M8hO3
PM2.5
M8hO3
PM2.5
year
(ppb)
(μg/m3)
(ppb)
(μg/m3)
(ppb)
(μg/m3)
2001
46.25
9
54
12.3
48.75
8
2050
41.75
6.425
46.25
8.425
44.25
6.125
2050 BAU
46
9.625
55.5
11.975
49.25
8.1
Notes: M8hO3 is the annual average of the daily 8-hour maximum ozone. PM2.5 is
the annual average of the daily PM2.5.
Infant mortality and PM2.5 projections
1. Number of births by region: NVSS (2012), assume constant
2. δIM/δPM from:
◦ Chay and Greenstone: 5.5 per 100k
◦ Knittel et al.: 18 per 100k
3. Conversion to PM2.5
◦ TSP to PM2.5: 0.16  CG 34 per 100k
◦ PM10 to PM2.5: 0.54  KMS 33 per 100k
4. δPM2.5/δCC from Tagaris et al.
5. Impact = 1 X 2 X 3 X 4
Table 3A. Infant mortality and
contemporaneous PM2.5
West
Plains
Midwest
Northeast
Southeast
All
Births
832,065
635,916
820,761
835,041
798,891
3,922,674
No
mitigation
Infant death vs. 2001
5,076
28
3,879
-11
5,007
133
5,094
177
4,873
-88
23,928
133
Mitigation
No
Mitigation
Mitigation
vs. no
mitigation Mitigation
vs. no
vs. 2001 mitigation vs. 2001
vs. 2001 mitigation
-113
-141
0.56%
-2.23%
-2.79%
-324
-314
-0.28%
-8.36%
-8.08%
-753
-886
2.65%
-15.05%
-17.70%
-731
-909
3.48%
-14.35%
-17.84%
-1,053
-964
-1.81%
-21.60%
-19.79%
-2,501
-2,634
0.56%
-10.45%
-11.01%
Adult earnings and PM2.5 projections
1. Per capita income by region (BEA REIS)
2. δearnings/δPM: 1.1% for 10 unit TSP (Isen et al.)
3. TSP to PM2.5: 0.68% for 1 unit PM2.5
4. δPM2.5/δCC from Tagaris et al.
5. Impact = 1 X 2 X 3 X 4
Table 3B. Adult earnings and early
childhood exposure to PM2.5
West
Plains
Midwest
Northeast
Southeast
All
Per capita
income
$44,589
$43,680
$41,548
$52,417
$38,550
$44,455
No
mitigation
vs. 2001
-$30
$15
-$134
-$222
$85
-$30
Mitigation
Mitigation
vs. no
vs. 2001 mitigation
$121
$151
$443
$429
$759
$893
$913
$1,135
$1,011
$926
$564
$594
No
Mitigation
mitigation Mitigation
vs. no
vs. 2001
vs. 2001 mitigation
-0.1%
0.3%
0.3%
0.0%
1.0%
1.0%
-0.3%
1.8%
2.1%
-0.4%
1.7%
2.2%
0.2%
2.6%
2.4%
-0.1%
1.3%
1.3%
Hospitalizations and ozone projections
1. δhosp/δozone from:
◦ Lleras-Muney
◦ 1 SD increase  8-23% decrease; 15.8% preferred
◦ 1 SD = .008 ppm
◦ 1 ppb ozone  1.97% change in hosp.
◦ Beatty & Shimshack
◦ 10% ozone increase  2.5-3.3% increase in hospitalizations; choose lowest
◦ 10% = 2.43 ppb
◦ 1 ppb ozone  1.03% change in hosp.
2. δozone/δCC from Tagaris et al.
3. Impact = 1 X 2
(No good baseline of hospitalizations by region)
Table 3C. Respiratory hospitalizations and
contemporaneous ozone
West
Plains
Midwest
Northeast
Southeast
All
No mitigation vs. 2001
BS
LM
0.0%
0.0%
0.8%
1.5%
0.0%
0.0%
-0.3%
-0.5%
1.5%
3.0%
0.5%
1.0%
Mitigation vs. 2001
BS
LM
-3.6%
-6.9%
-4.1%
-7.9%
-4.9%
-9.4%
-4.6%
-8.9%
-8.0%
-15.3%
-4.6%
-8.9%
Mitigation vs. no
mitigation
BS
LM
-3.6%
-6.9%
-4.9%
-9.4%
-4.9%
-9.4%
-4.4%
-8.4%
-9.5%
-18.2%
-5.1%
-9.9%
Other countries
Comparable wealth to US: comparable effects
LMIC could be different
◦ More rapid development, higher levels of pollution
◦ Past US pollution like current pollution
Figure 1. Trends in Air Pollution for US,
China, and Mexico
China
Pittsburgh
Mexico
Other countries
Comparable wealth to US: comparable effects
LMIC could be different
◦ More rapid development, higher levels of pollution
◦ Past US pollution like current pollution
◦ Arceo-Gomez et al: dose-response comparable (Mexico vs. US)
◦ Non-linear relationship (log(y)): larger effects
◦ Often equatorial  larger temperature increases
◦ If unabated, ozone increases likely bigger
◦ Less likely to mitigate. E.g., Kyoto
◦ No co-benefits
Suggest pollution via CC will have larger effects in LMIC countries
Conclusion
Unabated climate change  minimal effects on child well-being (relative to baseline)
Mitigating climate change  large “co-benefits” (relative to BAU and baseline)
◦
◦
◦
◦
Reduced infant mortality
Reduced respiratory illnesses
Improved test scores
Increased earnings
Many unknowns involved in projections
◦ Suggest immediate benefits from mitigation policies
◦ Not for geoengineering (CCS, solar radiation management)
◦ Useful starting point